





'''simple docstring'''




import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  from .features import FeatureType


lowercase__      ,				lowercase__      ,				lowercase__       : Optional[int]         =       False, False, False


@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			None
     lowerCAmelCase						     =			True
     lowerCAmelCase						     =			True
     lowerCAmelCase						     =			None
     # Automatically constructed
     lowerCAmelCase						     =			"dict"
     lowerCAmelCase						     =			pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}				)
     lowerCAmelCase						     =			field(default='''Audio'''   ,						init=a__   ,						repr=a__				)


     def __call__(		self):

          '''simple docstring'''






          return self.pa_type


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          try:
               import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
          except ImportError as err:
               raise ImportError('To support encoding audio data, please install \'soundfile\'.') from err
          if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
               return {"bytes": None, "path": value}
          elif isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
               return {"bytes": value, "path": None}
          elif "array" in value:
               # convert the audio array to wav bytes
               __A		:					Optional[int]						=			BytesIO()
               sf.write(_UpperCAmelCase		,      value['array']		,      value['sampling_rate']		,      format='wav')
               return {"bytes": buffer.getvalue(), "path": None}
          elif value.get('path') is not None and os.path.isfile(value['path']):
               # we set "bytes": None to not duplicate the data if they're already available locally
               if value["path"].endswith('pcm'):
                    # "PCM" only has raw audio bytes
                    if value.get('sampling_rate') is None:
                         # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
                         raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object')
                    if value.get('bytes'):
                         # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
                         __A		:					Tuple						=			np.frombuffer(value['bytes']		,      dtype=np.intaa).astype(np.floataa) / 3_2767
                    else:
                         __A		:					List[Any]						=			np.memmap(value['path']		,      dtype='h'		,      mode='r').astype(np.floataa) / 3_2767

                    __A		:					Optional[Any]						=			BytesIO(bytes())
                    sf.write(_UpperCAmelCase		,      _UpperCAmelCase		,      value['sampling_rate']		,      format='wav')
                    return {"bytes": buffer.getvalue(), "path": None}
               else:
                    return {"bytes": None, "path": value.get('path')}
          elif value.get('bytes') is not None or value.get('path') is not None:
               # store the audio bytes, and path is used to infer the audio format using the file extension
               return {"bytes": value.get('bytes'), "path": value.get('path')}
          else:
               raise ValueError(
                   F'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.')


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if not self.decode:
               raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.')

          __A					,__A		:					Any						=			(value['path'], BytesIO(value['bytes'])) if value['bytes'] is not None else (value['path'], None)
          if path is None and file is None:
               raise ValueError(F'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.')

          try:
               import librosa
               import soundfile as sf
          except ImportError as err:
               raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.') from err

          __A		:					Tuple						=			xsplitext(_UpperCAmelCase)[1][1:].lower() if path is not None else None
          if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
               raise RuntimeError(
                   'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
                   'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ')
          elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
               raise RuntimeError(
                   'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
                   'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ')

          if file is None:
               __A		:					List[str]						=			token_per_repo_id or {}
               __A		:					Optional[int]						=			path.split('::')[-1]
               try:
                    __A		:					str						=			string_to_dict(_UpperCAmelCase		,      config.HUB_DATASETS_URL)['repo_id']
                    __A		:					Union[str, Any]						=			token_per_repo_id[repo_id]
               except (ValueError, KeyError):
                    __A		:					List[Any]						=			None

               with xopen(_UpperCAmelCase		,      'rb'		,      use_auth_token=_UpperCAmelCase) as f:
                    __A					,__A		:					Optional[Any]						=			sf.read(_UpperCAmelCase)

          else:
               __A					,__A		:					Tuple						=			sf.read(_UpperCAmelCase)

          __A		:					Union[str, Any]						=			array.T
          if self.mono:
               __A		:					Optional[Any]						=			librosa.to_mono(_UpperCAmelCase)
          if self.sampling_rate and self.sampling_rate != sampling_rate:
               __A		:					List[Any]						=			librosa.resample(_UpperCAmelCase		,      orig_sr=_UpperCAmelCase		,      target_sr=self.sampling_rate)
               __A		:					Optional[Any]						=			self.sampling_rate

          return {"path": path, "array": array, "sampling_rate": sampling_rate}


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          from .features import Value

          if self.decode:
               raise ValueError('Cannot flatten a decoded Audio feature.')
          return {
              "bytes": Value('binary'),
              "path": Value('string'),
          }


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          if pa.types.is_string(storage.type):
               __A		:					str						=			pa.array([None] * len(_UpperCAmelCase)		,      type=pa.binary())
               __A		:					Optional[int]						=			pa.StructArray.from_arrays([bytes_array, storage]		,      ['bytes', 'path']		,      mask=storage.is_null())
          elif pa.types.is_binary(storage.type):
               __A		:					Optional[Any]						=			pa.array([None] * len(_UpperCAmelCase)		,      type=pa.string())
               __A		:					str						=			pa.StructArray.from_arrays([storage, path_array]		,      ['bytes', 'path']		,      mask=storage.is_null())
          elif pa.types.is_struct(storage.type) and storage.type.get_all_field_indices('array'):
               __A		:					Dict						=			pa.array([Audio().encode_example(_UpperCAmelCase) if x is not None else None for x in storage.to_pylist()])
          elif pa.types.is_struct(storage.type):
               if storage.type.get_field_index('bytes') >= 0:
                    __A		:					Tuple						=			storage.field('bytes')
               else:
                    __A		:					Any						=			pa.array([None] * len(_UpperCAmelCase)		,      type=pa.binary())
               if storage.type.get_field_index('path') >= 0:
                    __A		:					List[Any]						=			storage.field('path')
               else:
                    __A		:					Dict						=			pa.array([None] * len(_UpperCAmelCase)		,      type=pa.string())
               __A		:					Optional[Any]						=			pa.StructArray.from_arrays([bytes_array, path_array]		,      ['bytes', 'path']		,      mask=storage.is_null())
          return array_cast(_UpperCAmelCase		,      self.pa_type)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          @no_op_if_value_is_null
          def path_to_bytes(_UpperCAmelCase):
               with xopen(_UpperCAmelCase		,      'rb') as f:
                    __A		:					Union[str, Any]						=			f.read()
               return bytes_

          __A		:					int						=			pa.array(
              [
                  (path_to_bytes(x['path']) if x['bytes'] is None else x['bytes']) if x is not None else None
                  for x in storage.to_pylist()
              ]		,      type=pa.binary()		,      )
          __A		:					Optional[int]						=			pa.array(
              [os.path.basename(_UpperCAmelCase) if path is not None else None for path in storage.field('path').to_pylist()]		,      type=pa.string()		,      )
          __A		:					Any						=			pa.StructArray.from_arrays([bytes_array, path_array]		,      ['bytes', 'path']		,      mask=bytes_array.is_null())
          return array_cast(_UpperCAmelCase		,      self.pa_type)
"""simple docstring"""


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class   __UpperCAmelCase		(      unittest.TestCase      ):
				A__ :  List[Any]    				=			StableDiffusionLDMaDPipeline
				A__ :  List[str]    				=			TEXT_TO_IMAGE_PARAMS
				A__ :  str    				=			TEXT_TO_IMAGE_BATCH_PARAMS
				A__ :  Tuple    				=			TEXT_TO_IMAGE_IMAGE_PARAMS

				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =UNetaDConditionModel(
										    block_out_channels=(32, 64)      ,					layers_per_block=2      ,					sample_size=32      ,					in_channels=4      ,					out_channels=4      ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,					cross_attention_dim=32      ,					)
										lowerCamelCase__   =DDIMScheduler(
										    beta_start=0.0_0_0_8_5      ,					beta_end=0.0_1_2      ,					beta_schedule="scaled_linear"      ,					clip_sample=_lowerCamelCase      ,					set_alpha_to_one=_lowerCamelCase      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =AutoencoderKL(
										    block_out_channels=[32, 64]      ,					in_channels=6      ,					out_channels=6      ,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,					latent_channels=4      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =CLIPTextConfig(
										    bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=32      ,					intermediate_size=37      ,					layer_norm_eps=1E-05      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1000      ,					)
										lowerCamelCase__   =CLIPTextModel(_lowerCamelCase )
										lowerCamelCase__   =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

										lowerCamelCase__   ={
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=0 ):
										if str(_lowerCamelCase ).startswith("mps" ):
																lowerCamelCase__   =torch.manual_seed(_lowerCamelCase )
										else:
																lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator

										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
										lowerCamelCase__   =np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )

										assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2

				def 						_a      ( self ):
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs["prompt"]]

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs.pop("prompt" )]

										lowerCamelCase__   =ldmad_pipe.tokenizer(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=ldmad_pipe.tokenizer.model_max_length      ,					truncation=_lowerCamelCase      ,					return_tensors="pt"      ,					)
										lowerCamelCase__   =text_inputs["input_ids"].to(_lowerCamelCase )

										lowerCamelCase__   =ldmad_pipe.text_encoder(_lowerCamelCase )[0]

										lowerCamelCase__   =prompt_embeds

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
										assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4






				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =PNDMScheduler(skip_prk_steps=_lowerCamelCase )
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   ="french fries"
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase      ,					negative_prompt=_lowerCamelCase )

										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
										lowerCamelCase__   =np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
										assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2






@slow
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 3,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1].flatten()
										lowerCamelCase__   =rgb[0, -3:, -1].flatten()

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512)

										lowerCamelCase__   =np.array(
										    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
										lowerCamelCase__   =np.array(
										    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
										assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
										assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3






@nightly
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 50,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_9_5_5_8_6
										lowerCamelCase__   =0.3_3_7_9_5_5_1_5
										lowerCamelCase__   =1_1_2.4_8_5_1_8
										lowerCamelCase__   =9_8.4_8_9_7_4_6
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_1_9_4_1_2_7
										lowerCamelCase__   =0.3_5_3_7_5_5_8_6
										lowerCamelCase__   =0.5_6_3_8_5_0_2
										lowerCamelCase__   =0.3_4_6_8_6_1_0_3

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512, 1)
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3


